Alexander Borisov
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Molecular Biology
- Information Systems
- Computational Theory and Mathematics
- Co-authors
- Eugene TuvGeorge C. RungerKari TorkkolaK. TorkkolaValery AlexeevEvgeniy O. KiktenkoAleksey K. FedorovMargherita Barile
- Topics
- Neural Networks and Applications (2 papers)Algebraic Geometry and Number Theory (2 papers)Mathematical Dynamics and Fractals (2 papers)
- Cited by
- Discrete Mathematics and CombinatoricsArtificial IntelligenceComputer Vision and Pattern Recognition
- Journals
- Journal of Machine Learning ResearchAmerican Mathematical MonthlyProceedings of the American Mathematical Society
- Partner nations
- United StatesItalyIreland
In The Last Decade
Alexander Borisov
12 papers receiving 262 citations
Peers
Comparison fields: 5 of 95
- Artificial Intelligence 123
- Computer Vision and Pattern Recognition 48
- Molecular Biology 43
- Information Systems 28
- Computational Theory and Mathematics 22
Countries citing papers authored by Alexander Borisov
This map shows the geographic impact of Alexander Borisov's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Alexander Borisov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexander Borisov more than expected).
Fields of papers citing papers by Alexander Borisov
This network shows the impact of papers produced by Alexander Borisov. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Alexander Borisov. The network helps show where Alexander Borisov may publish in the future.
Co-authorship network of co-authors of Alexander Borisov
This figure shows the co-authorship network connecting the top 25 collaborators of Alexander Borisov. A scholar is included among the top collaborators of Alexander Borisov based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Alexander Borisov. Alexander Borisov is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 25 | |
| 2 | 5 | |
| 3 | Active Batch Learning with Stochastic Query-by-Forest (SQBF) | 8 |
| 4 | 1 | |
| 5 | 8 | |
| 6 | 2 | |
| 7 | 2 | |
| 8 | Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination | 197 |
| 9 | 4 | |
| 10 | 5 | |
| 11 | 14 | |
| 12 | 7 |
About Alexander Borisov
Alexander Borisov is a scholar working on Architecture, Geometry and Topology and Algebra and Number Theory, having authored 12 papers that have together received 278 indexed citations. Recurring topics across this work include Neural Networks and Applications (2 papers), Algebraic Geometry and Number Theory (2 papers) and Mathematical Dynamics and Fractals (2 papers). The work is most often cited by research in Discrete Mathematics and Combinatorics (15 citations), Artificial Intelligence (123 citations) and Computer Vision and Pattern Recognition (48 citations). Alexander Borisov has collaborated with scholars based in United States, Italy and Ireland. Frequent co-authors include Eugene Tuv, George C. Runger, Kari Torkkola, K. Torkkola, Valery Alexeev, Evgeniy O. Kiktenko, Aleksey K. Fedorov, Margherita Barile, Mark Sapir and S. P. Hastings. Their work appears in journals such as Journal of Machine Learning Research, American Mathematical Monthly and Proceedings of the American Mathematical Society.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.